Maximum power point tracking for wind energy system by adaptive neural-network based fuzzy inference system

Wind generation system is being a major share of grid due to due to pollutions and environmental issues accompanied with conventional sources. Even though wind energy is abundant but wind velocity is uncertain. To accomplish the change in wind speed it is essential to adopt control strategy to track maximum power regardless to the wind speed variation. For obtaining maximum efficacy the maximum power point tracking (MPPT) controller design has improved attention. Most MPPT schemes either rely on wind speed measurement or on complex estimations and online calculations. Hence these techniques are either costly due to requirement of wind speed sensors or suffer from inaccuracy due to dissimilarities in wind turbine models. To overcome these types of problem, a novel self-tuning MPPT by using adaptive neuro fuzzy inference system (ANFIS) is proposed in this paper. To confirm the effectiveness of proposed MPPT analysis, simulation of the proposed algorithm is being verified for Doubly-Fed Induction Generator (DFIG) based wind system using MATLAB Simulink environment.